Discovering Biological Meaning in Large, Complex Networks

Using network resources available on StemSight, you can easily explore subnetworks centered on your genes of interest and use them as a starting point to mine additional predicted functional relationships and connections in our mESC predictive network.

  1. Create Gene and Edge Lists
    To download gene and edge details for interactomes created with StemSight Scout, click on SHOW CONTROLS. Press Genes & SRC to retrieve a tab-delimited text file containing a list of all genes in your network view, including the official gene symbol, MGI:ID, and Self-Renewal Correlation (SRC) score for each gene. Press Edges & Inf. Scores to retrieve a tab-delimited text file with with the official gene symbol MDGI:ID for each gene pair in an edge and posterior inference score for each edge. To retrieve more information about network connections for your genes of interest, you may want to download the mESC network graph file and write scripts to extract a more specific set of subnetwork edges. To compare lists of genes involved in different subnetworks, try using interactive Venn Diagram tools, such as VENNY.
  2. Perform GO Term Enrichment
    Open Access functional fenomics annotation tools such as DAVID and VLAD will help you find biological meaning in gene lists extracted from network views.
  3. Identify Novel Players and Design Experiments
    As you identify genes and gene products you want to investigate further, refer to the underlying network data sources to consider what experiments others labs may have done to assess the molecular basis of self renewal processes.

Stemsightful Views

Here are a couple of examples to give you ideas for creating special network views of your own.

A Comparative View of WNT Signaling Participants

To create models of the WNT signaling pathway using predictive network data, we first created a list of pathway edges based on the KEGG Wnt signaling pathway for Mus musculus. We then wrote a python script to extract posterior edge weights from each network graph file, and used Cytoscape to create a custom pathway view in which we used SRC scores to determine node color and posterior edge weights to define edge width and color. We annotated the final image file using Adobe Illustrator.

Wnt Signaling Pathway in the mESC
	and Negative Control networks

Viewing Tdh Connections to mESC Gold Standard Genes

To visualize Tdh connections to high-confidence "golden" gold standard genes in the mESC network, we used Scout to create a Tdh-centered interactome and set the Maximum edges per node to 32 and the Minimum inference score to 0.9997. We removed nodes not included in the golden gold standard by right- (or Control-) clicking on individual nodes and selecting Hide selected node. With all genes in the Scout view "frozen," we rearranged the nodes into a circular graph and copied and pasted the subnetwork into Adobe Illustrator to create an annotated image file with SRCs and edge weights.

Tdh connections to golden gold standard genes